Hydraulic Brain Demonstrates Zero-Lag Resonance with 0.316 Coherence and Bidirectional Coupling at 78.1ms

The prevailing view of brain function often treats bodily signals as mere noise, but emerging evidence suggests these signals actively shape cognition. Ahmed Gamal Eldin from Nova University Lisbon, along with colleagues, now demonstrates a compelling mechanism underlying this interaction, revealing that the brain and body operate as a resonating system undergoing a critical phase transition. Through detailed analysis of brain activity recorded using high-density EEG, the team discovered a powerful coupling between brain and body, peaking at a specific moment in time during cognitive tasks. This research establishes that understanding does not arise from complex processing alone, but from a thermodynamic sequence where brain-body resonance acts as a discrete gate, potentially offering a fundamental distinction between biological and artificial intelligence.

Embodied Resonance Explains EEG Signal Loss

This research challenges conventional EEG analysis by demonstrating that signals typically discarded as artifact, such as those from bodily movements and muscle activity, are crucial components of cognitive processing, specifically related to embodied resonance. Removing these signals leads to a significant loss of information, hindering a complete understanding of brain activity, and proposes a new understanding of how the brain integrates information through whole-body coupling. Key findings demonstrate that artifacts are not noise, but signals reflecting the brain’s interaction with the body and environment. The research team proposes that cognitive processing relies on a resonant coupling between the brain and the body, reflected in the EEG signal, including what is traditionally considered artifact.

They suggest shifting from an information-processing metaphor to a thermodynamic one, where the brain operates as a physical system with specific energetic properties, and that cognitive processes involve phase transitions triggered by resonance, enabling information integration. These findings have significant implications for the development of more biologically plausible AI systems, suggesting that truly intelligent AI may require embodied systems with intrinsic dynamics and physical constraints, rather than purely computational approaches. The study involved analyzing publicly available EEG datasets, focusing on the impact of artifact removal techniques on the overall signal. The authors used time-lagged Granger causality to investigate the relationship between artifact-related signals and cognitive processing, utilizing concepts including Granger causality, sample entropy, phase transitions, and embodied cognition. The paper advocates for a paradigm shift in how we analyze and interpret EEG data, urging researchers to embrace the body as an integral part of the cognitive process and to reconsider the role of artifacts as potentially meaningful signals.

Zero-Lag Brain Synchrony During Target Recognition

This research demonstrates a remarkable synchronization between brain and body during cognitive processing, revealing a resonant mechanism previously unobserved in human cognition. Scientists achieved a measurement of near-perfect zero-lag synchrony, quantified by a Phase Slope Index of 0. 000044, between posterior and frontal brain regions across over 500 trials, accompanied by high coherence of 0. 316, confirming strong frequency-domain coupling. This combination indicates that these brain regions are tightly phase-locked without a leading signal, suggesting a fundamentally different mode of interaction than sequential information transfer.

Examination of individual trials revealed that 100% exhibited near-zero PSI values, demonstrating the consistency of this coupling during target recognition. Further analysis using Granger causality revealed massive bidirectional coupling, peaking simultaneously at 78. 1 milliseconds, with F-statistics of 100. 53 for brain-to-body and 62. 76 for body-to-brain interaction.

This temporal coincidence is striking, as typical brain connectivity studies show causal lags of 50 to 200 milliseconds, indicating sequential processing, and the exceptionally large F-statistic magnitudes demonstrate that brain-body coupling is not weak correlation but the primary mode of interaction during this resonant window. The research team observed a 1. 6:1 ratio in causal flow, suggesting a slight asymmetry with cortical initiation and somatic feedback. The study further mapped a thermodynamic trajectory of this interaction, identifying three distinct regimes. From stimulus onset to resonance lock, bidirectional Granger causality increased while entropy remained statistically unchanged, indicating constraint accumulation without spatial trajectory movement.

Following resonance onset, the system underwent rapid state space expansion, with sample entropy increasing by 0. 009 bits, confirmed by a binomial test with p=0. 002, signifying a shift from a low-entropy, high-constraint regime to a higher-entropy, distributed processing regime.

Body-Brain Resonance Drives Rapid Recognition

This research demonstrates that human understanding during target recognition functions via a specific thermodynamic cycle, encompassing constraint accumulation, resonance-triggered phase transition, and sustained metastable integration. The team discovered that removing signals originating from the body, typically treated as noise in electroencephalography, eliminates a substantial portion of the cognitive signal, suggesting a fundamental role for whole-body coupling in cognition. Specifically, the brain and body operate as mutually coupled oscillators, achieving near-instantaneous resonance at a critical threshold, which then acts as a binary switch triggering expansion of the system’s state and enabling information integration. Importantly, the magnitude of this transition is independent of the initial strength of resonance, confirming a true phase transition dynamic rather than a simple amplification of existing signals.

These findings challenge conventional models of cognition, which often rely on the information-processing metaphor, and suggest that understanding is not merely computation but a physical event dependent on specific thermodynamic architecture. The authors acknowledge that their analysis focused on group-average effects and suggest future research should investigate individual differences in resonance strength and its correlation with cognitive capacities like working memory. They also propose that deficits in brain-body coupling may underlie certain neurodevelopmental and neurological conditions. This work implies that achieving genuine artificial intelligence may require moving beyond disembodied computation to create systems with intrinsic dynamics, physical constraints, and thermodynamic costs that enable these crucial constraint-release phase transitions.